Kyra Henriques
Abstract: Microplastics are proven to be harmful to living organisms. There are about 51 trillion microplastics (< 5mm size) present in water bodies worldwide. Filters, membranes, and sieves currently used to capture aquatic microplastics are costly and labor-intensive, limiting widespread usage. Ferrofluids, made up of iron oxide (Fe3O4) and oil, are cheaper alternatives. Ferrofluids exploit the hydrophobic properties of microplastics and oil, allowing the removal of microplastics through magnets. For this research, varying volumes of used and unused cooking oils and engine oils were combined with different weights of Fe3O4, to synthesize ferrofluids. These solutions were then used to extract Polypropylene (PP), Polyethylene (PE), and Polyethylene Terephthalate (PET) (< 2mm sized) microplastics from water, and the microplastic removal efficiencies (MRE) were calculated. The goal was to understand the effect of different oils, oil volumes, and Fe3O4 weights on microplastic removal efficiency. This study also aimed to determine the ideal ferrofluid composition with a high MRE. This ferrofluid combination was used in an electromechanical prototype, designed using the Raspberry Pi, which was built to fully automate the microplastic extraction process. The results suggest an inverse relationship between oil volume and MRE. Unused cooking oil and used engine oil had the highest and lowest MRE respectively. For each of the three microplastic types extracted using the prototype, the average MRE was observed to be greater than 85%. Laboratory and prototype investigations indicate that a high MRE is possible, illustrating that ferrofluids used to magnetically remove microplastics are a viable solution to the increasing aquatic microplastics problem.
Please click here for the full article.
Matthew Uppani
Abstract: Most chemotherapy drugs kill cancer cells by inhibiting the metabolic functions of those cells. In the process of killing cancer cells, chemotherapy drugs can also act upon healthy cells, which gives rise to adverse side effects. Current studies have examined the cytotoxic effect of a cancer drug, 5-fluorouracil, and the effect of a combination of vitamins A, E, and melatonin on abating oxidative stress on cellular membranes, thus minimizing toxicity. This study uses spot plating, microscopic analysis, and liquid turbidity test to examine the effects of antioxidants on 5-fluorouracil. Spot plating provides a visual representation of 5-fluorouracil’s influence on cell growth in various conditions, over the period of one day. The microscopic analysis assesses the membrane integrity of cells at 2500x magnification. Liquid media tests provide a statistical representation of the cell growth trend (OD600) in two hours. The addition of a combination of vitamins A, E, and melatonin prevented 94% of cell death caused by 5-fluorouracil, providing new insights into chemotherapy treatment. This study suggests that 5-fluorouracil toxicity is due to oxidative stress on cell membranes.
Please click here for the full article.
Kedar Chintalapati
Abstract: With growing computational abilities, machine learning is becoming more applicable in drug discovery. However, the entire process of drug discovery can take over 12 years and cost approximately 2.6 billion dollars, rendering it intensive in both time and physical resources. One of the main steps in non-clinical development is hit identification and discovery, in which drug molecules with desired efficacies are identified. In silico modeling of protein expression changes from drugs could greatly speed this up, as changes in protein expression determine the effectiveness of drugs. In this study, a framework has been developed and validated to predict the impacts of drug molecules on certain proteins’ expressions through a machine learning approach. Through the use of three distinct molecular featurization techniques – molecular fingerprints and numerical properties extracted using two different libraries – several machine learning models were trained to predict the impacts of drugs on protein expression of Caspase-3. The best result in this study was from a convolutional neural network (CNN) trained on molecular fingerprints data, with similar results from some other algorithms and featurization-methods; the CNN model made effective generalizations between drugs and protein expression, demonstrated by its accuracies on both balanced, (oversampled minority class) SMOTE-augmented data, and original, imbalanced test data. This framework can be applied to early stages of drug discovery to develop more machine learning models on more proteins and use them to speed up and cheapen the process through their abilities to generalize on the relations between drug molecules and changes in protein expression.
Please click here for the full article.
Emma Ka
Abstract: Previous research has shown that risk for cardiovascular disease (CVD) is inversely associated with high-density lipoprotein (HDL) functionality. HDL possesses certain cardioprotective properties enabling it to promote the efflux of cholesterol, which then prevents cholesterol buildup on the artery walls. Apolipoprotein A-I (apoA-I) is the major protein component of HDL. However, apoA-I’s transition between its lipid-free and HDL-bound states, a property indicative of HDL functionality, remains poorly understood. This study set out to characterize this transition through kinetic analysis of HDL-apolipoprotein-A-I-exchange (HAE) assays using reconstituted HDL (rHDL). Our findings support our hypothesis that smaller rHDL particle size and use of an apoA-I genetic variant, apoA-I milano, produce greater rates of exchange and thus enhanced HDL functionality.
Please click here for the full article.
Rishabh Ranjan and Gopalaniruddh Tadinada
Abstract: Pancreatic Ductal Adenocarcinoma (PDAC) is a form of cancer with a 5-year survival rate of 8%. PDAC is hardly detected in its premalignant stages (when surgery is viable), due to modern screening method limitations. This study utilizes a panel of miRNA biomarkers that express TFF1, REG1B, and LYVE1, combined with age, gender, and creatinine, for a diagnostic test to screen for PDAC. This panel of biomarkers is an indicator of increased desmoplasia, or the growth of fibrous connective tissue surrounding pancreatic acinar cells. In addition, a low-cost qPCR solution was also developed to amplify and quantify the biomarker levels from patient samples. The device uses an Arduino-controlled PID loop for the thermocycling of the miRNA. Level extraction is done utilizing a fluorescence detection algorithm coupled with a scientific CMOS (sCMOS) camera. Cycle times, amplification curves, and temperature curves are displayed on a web interface for the user. Once the levels are extracted, they can be inputted into the XGBoost algorithm for a PDAC Screen. When compared to the BioRad 384 well system, the qPCR achieved statistically similar quantifications, as well as quick thermal cycling. With an XGBoost approach, PDAC can be detected with 0.9812 AUC and 93.18% accuracy. This solution can be implemented as a standard of care due to the noninvasive, convenient, and low cost nature of urine tests.
Please click here for the full article.
Olivia Morrissey
Abstract: Bioprinting is the use of 3D printing technology, living cells, and bioinks to produce constructs. Bioprinting is a powerful tool that can help regenerate tissue in the body or print organs to be used in transplants. Although discoveries, such as the development of new printing methods and bioinks, have helped the scientific community learn more about the challenges of the bioprinting process, in vivo bioprinting, bioprinting that takes place directly in the body, is a common goal that many researchers are striving to achieve. This paper will review successful bioprinting processes and the different bioinks that have emerged. Also, the paper will offer new perspectives on how robot-assisted surgery could be combined with in vivo bioprinting to create new surgical technology, including injuries to the musculoskeletal system. The musculoskeletal system requires long and invasive procedures to fix problems that occur. Most musculoskeletal injuries are caused by strains on muscles that lead to tears in ligaments and the need for soft tissue replacement. Through in vivo bioprinting, the reconstruction and regeneration of skeletal tissue without long and invasive procedures is possible. Utilization of bioprinting could limit the need for complicated surgeries. Also, it would require the implementation of surgical robots.
Please click here for the full article.
Edward S. Ju
Abstract: The conventional smart window plays a passive role in reducing the sunlight entering the window by adjusting the transmittance of the transparent window. Recently, there has been a demand for the development of a next-generation smart window that can provide information in the form of an image through color change in a certain area of the window. In this study, a flexible and transparent hydroxypropyl cellulose (HPC) display was developed based on the thermochromic properties of HPC hydrogels. HPC changes its hydrophilic/hydrophobic properties based on its lower critical solution temperature (LCST, ~48.0 °C). Above the LCST, the molecular chains of cellulose, which are hydrogen-bonded to water molecules, break, causing cellulose molecules to agglomerate and resulting in white color formation. Based on this mechanism, a display image can be realized by increasing the temperature of a specific area of an HPC film by using a near-infrared laser. This technology will expand the application scope of future smart window technologies that require real-time information without the integration of complicated electric circuits.
Please click here for the full article.
Meghan Tuttle
Abstract: Spinal cord injury (SCI) is a life-altering condition that is especially difficult to heal due to the genetic makeup of the central nervous system (CNS) and inhibitory molecules present in SCI lesion sites. A variety of proteins in SCI lesion sites transduce inhibitory signals, preventing neurite outgrowth. One of these proteins is semaphorin 3A (Sema3A), which collapses growth cone expansion by dissembling actin networks. Xanthofulvin (SM-216289), a drug cultured from Penicillium, has been identified as a selective Sema3A inhibitor that blocks Sema3A from binding to its receptors. Therefore, it is suspected to inhibit growth cone collapse in a pharmaceutical setting. This review investigates the efficacy of SM-216289 in promoting axon regeneration and functional recovery in vitro and in vivo for SCI, specifically, by analyzing a study conducted by Kaneko and colleagues in 2006. There is convincing evidence that SM-216289 is efficacious in inhibiting growth cone collapse and enhancing functional recovery, as enhanced upregulation of growth-associated proteins, migration of Schwann cells, and reestablishment of hindlimb control were observed in SM-216289 treated rats. However, further research must be conducted regarding the drug’s integration with other therapeutic strategies.
Please click here for the full article.
Antonia Kolb
Abstract: There is an alarming increase in the population of ticks and tick-borne diseases (TBDs), some of which are fatal. Due to limited training, healthcare providers cannot always accurately identify ticks and their associated illnesses, leading to delayed diagnoses and treatments. The prevalence rates of different disease-causing pathogens vary based on geographic locations within the United States. A convolutional neural network (CNN) was built for combining real-time tick-species identification with location-based tick-risk assessment by embedding the Pennsylvania Tick Research Lab’s spatio-temporal tick surveillance statistics. With DETICKT IT, an iOS app developed in Swift with a Python backend, users can now receive an accurate and conclusive analysis to determine whether they are at risk of contracting a certain TBD. The app was able to accurately identify three tick species: Ixodes scapularis (Eastern blacklegged tick), Amblyomma americanum (Lone star tick), and Dermacentor variabilis (American dog tick). The overall accuracy rate of the model is approximately 80%. This app shows promise in assisting tick bite victims with their decision of seeking further medical assistance, particularly those with underlying health conditions.
Please click here for the full article.
Han Chung
Abstract: Antibiotic resistance is a major concern for public health worldwide. The CRISPR-Cas13 system is a gene editing technology derived from an acquired defense mechanism in bacteria. CRISPR-Cas13 is a potential candidate for the development and screening of novel antimicrobials that could curb antibiotic resistance and help to identify harmful pathogens. This literature review examines the history of antibiotics and antibiotic resistance, the CRISPR-Cas13 mechanism, and notable applications of CRISPR-Cas13 in antibiotic development and bacterial screening. In addition, the review discusses the drawbacks of this technology and makes comparisons of CRISPR-Cas13-based applications to traditional antimicrobials and screening tests such as reverse transcription polymerase chain reaction (RT-PCR).
Please click here for the full article.
Marvin Oprean
Abstract: This research aims to assess the impact of acute kidney injury (AKI) on the outcomes of elective or emergency cardiac surgery patients. The group included 183 aortic disease patients whose serum creatinine (SCr) level was measured at admission, on the first day of intensive care, and upon hospital discharge. The results indicate that SCr levels and immediate mortality during hospitalisation are significantly correlated, but there is no significant correlation between SCr level and long-term mortality at least six months after discharge was detected. These findings are relevant for future research on managing patients with high SCr levels. The study also provides essential information for surgical cardiac patient management.